Lenovo ThinkSystem SR780a V3 GPU Server 8x PCIe GPU 4U

The Lenovo ThinkSystem SR780a V3 is a 4U rack-optimized GPU server engineered around AMD EPYC 9005 series processors, delivering a purpose-built platform for the most demanding generative AI training, large language model fine-tuning, and high-performance computing workloads. The system accommodates up to eight full-height, double-wide PCIe 5.0 GPUs, supporting NVIDIA H100 and H200 SXM or PCIe variants as well as AMD Instinct MI300X accelerators, providing aggregate GPU memory capacity and interconnect bandwidth required for trillion-parameter model training runs. The architecture pairs dual EPYC 9005 processors—featuring up to 192 cores per socket with AMD Zen 5 microarchitecture—with high-bandwidth DDR5 memory across 24 DIMM slots, ensuring host-side compute and memory throughput do not bottleneck GPU utilization.

The SR780a V3 features a high-efficiency thermal design with optimized airflow pathways and redundant hot-swap fans calibrated for sustained full-load GPU operation in data center environments. PCIe 5.0 connectivity across all eight GPU slots maximizes bidirectional bandwidth between accelerators and the CPU complex, while NVLink or Infinity Fabric interconnects between GPUs are supported depending on the accelerator installed. Flexible storage options include front-accessible NVMe U.2 or E3.S drives for high-speed dataset staging and checkpoint storage, complemented by rear storage bays for capacity drives.

As part of Lenovo's ThinkSystem V3 portfolio launched in 2025, the SR780a V3 integrates with Lenovo XClarity Administrator for unified lifecycle management, firmware orchestration, and telemetry-driven predictive maintenance. The server supports standard OpenBMC-based out-of-band management, PCIe bifurcation for flexible I/O configuration, and is optimized for deployment in AI supercomputing clusters, enterprise AI factories, and cloud service provider GPU pods where density, scalability, and operational reliability are paramount.

Ideal for

• Large-scale generative AI and large language model pre-training using multi-GPU tensor and pipeline parallelism across NVIDIA H100/H200 or AMD MI300X accelerators
• Enterprise AI fine-tuning and RLHF workflows requiring high aggregate GPU memory capacity for loading and training multi-hundred-billion-parameter foundation models
• High-performance computing simulations in computational fluid dynamics, molecular dynamics, and climate modeling leveraging GPU-accelerated solver frameworks
• AI inference serving at scale for latency-sensitive applications requiring maximum GPU density per rack unit in cloud and colocation environments
• Multi-framework deep learning research infrastructure supporting PyTorch, JAX, and TensorFlow distributed training across homogeneous GPU clusters
• GPU cluster node deployment within AI supercomputing fabrics utilizing high-speed InfiniBand or RoCE networking for inter-node collective communication

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.